Browsing by Author "Thomas, L."

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A Healthcare management using clinical decision support system
(Institute of Electrical and Electronics Engineers Inc., 2018) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
From the literature it is studied that, most of the medical error is due to faulty healthcare system. Due to this, there is treatment delay, that leads to complications in later stages of disease progression. Medical error caused due to the failure in healthcare system can be reduced by employing an appropriate clinical decision support system (CDSS). CDSS helps in identifying the severity of disease by predicting its progression. The treatment management of gallstone disease is considered as a case study in this paper.This paper presents a CDSS with the help of machine learning for improving the treatment management. CDSS with the help of a statistical comparator, identifies an efficient tool for finding the associated risk factors. These risk factors are then used to predict the disease progression and identify the cases that may need Endoscopic Retrograde Cholangio-Pancreatography (ERCP) as the treatment progresses. The model that learns and predicts accurately is selected, using the concept of Area Under Curve (AUC). For this purpose, a Modified Cascade Neural Network (ModCNN) built upon the architecture of Cascade-Correlation Neural Network (CCNN) is proposed and tested using an ADAptive LInear NEuron (ADALINE) circuit. It's performance is evaluated and compared with Artificial Neural Network (ANN) and CCNN.Using this prediction information, disease progression is analysed and proper treatment is initiated, thereby reducing the medical error. ModCNN showed better accuracy (96.42%) for predicting the disease progression when compared with CCNN (93.24%) and ANN (89.65%). Thus, CDSS presented here, assisted in reducing the medical error and providing better healthcare management. Â© 2018 IEEE.
Alphabetic cryptography: Securing communication over cloud platform
(2019) Cowlessur, S.K.; Annappa, B.; Manoj, Kumar, M.V.; Thomas, L.; Sneha, M.M.; Puneetha, B.H.
This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. � Springer Nature Singapore Pte Ltd. 2019.
Alphabetic cryptography: Securing communication over cloud platform
(Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Manoj Kumar, M.V.; Thomas, L.; Sneha, M.M.; Puneetha, B.H.
This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. Â© Springer Nature Singapore Pte Ltd. 2019.
An online decision support system for recommending an alternative path of execution
(Institute of Electrical and Electronics Engineers Inc., 2017) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
Traditional process execution follow the path of execution drawn by the process analyst without observing the behaviour of resource and other real time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: yMiner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique for identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour yMiner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimized by 59% by decreasing their load. Â© 2017 IEEE.
An optimal process model for a real time process
(CEUR-WS, 2015) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.; Vishwanath, K.P.
Recommending an optimal path of execution and a complete process model for a real time partial trace of large and complex organization is a challenge. The proposed AlfyMiner (Î±yMiner) does this recommendation in cross organization process mining technique by comparing the variants of same process encountered in different organization. Î±yMiner proposes two novel techniques Process Model Comparator (Î±yComp) and Resource Behaviour Analyser (RBAMiner). Î±yComp identifies Next Probable Activity of the partial trace along with the complete process model of the partial trace. RBAMineridentifies the resources preferable for performing Next Probable Activity and analyse their behaviour based on performance, load and queue. Î±yMiner does this analysis and recommend the best suitable resource for performing Next Probable Activity and process models for the real time partial trace. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending NPA and the performance of resources were optimized by 59% by decreasing their load.
Application of parallel K-means clustering algorithm for prediction of optimal path in self aware mobile ad-hoc networks with link stability
(2011) Thomas, L.; Annappa, B.
Providing Quality of Service (QoS) in terms of bandwidth, delay, jitter, throughput etc., for Mobile Ad-hoc Network (MANET) which is the autonomous collection of nodes, is challenging issue because of node mobility and the shared medium. This work is to predict the Optimal link based on the link stability which is the number of contacts between 2 pair of nodes that can be effectively applied for prediction of optimal effective path while taking QoS parameters into account to reach the destination using the application of K-Means clustering algorithm for automatically discovering clusters from large data repositories which is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the optimal effective path from source to sink. The work optimizes the previous result by pre-assigning task for finding the best stable link in MANET and then work is explored only on that stable link hence, by doing so we are able to predict the optimal path in more time efficient way. � 2011 Springer-Verlag.
Application of parallel K-means clustering algorithm for prediction of optimal path in self aware mobile ad-hoc networks with link stability
(2011) Thomas, L.; Annappa, B.
Providing Quality of Service (QoS) in terms of bandwidth, delay, jitter, throughput etc., for Mobile Ad-hoc Network (MANET) which is the autonomous collection of nodes, is challenging issue because of node mobility and the shared medium. This work is to predict the Optimal link based on the link stability which is the number of contacts between 2 pair of nodes that can be effectively applied for prediction of optimal effective path while taking QoS parameters into account to reach the destination using the application of K-Means clustering algorithm for automatically discovering clusters from large data repositories which is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the optimal effective path from source to sink. The work optimizes the previous result by pre-assigning task for finding the best stable link in MANET and then work is explored only on that stable link hence, by doing so we are able to predict the optimal path in more time efficient way. Â© 2011 Springer-Verlag.
Best resource recommendation for a stochastic process
(International Information Institute Ltd. No. 509 Fujimi-Cho 6-64-3 Tachikawa City, Tokyo 190-0013, 2016) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
The aim of this study was to develop an Artificial Neural Network's recommendation model for an online process using the complexity of load and performance of the resources. The proposed model investigate the resource performance using stochastic gradient decent method and probabilistic cost function for learning ranking function. The test result of CoSeLoG project is presented with accuracy of 72.856%. © 2016 International Information Institute.
Capturing the sudden concept drift in process mining
(2015) Manoj, Kumar, M.V.; Thomas, L.; Annappa, B.
Concept drift is the condition when the process changes during the course of execution. Current methods and analysis techniques existing in process mining are not proficient of analyzing the process which has experienced the concept drift. State-of-the-art process mining approaches consider the process as a static entity and assume that process remains same from beginning of its execution period to end. Emphasis of this paper is to propose the technique for localizing concept drift in control-flow perspective by making use of activity correlation strength feature extracted using process log. Concept drift in the process is localized by applying statistical hypothesis testing methods. The proposed method is verified and validated on few of the real-life and artificial process logs, results obtained are promising in the direction of efficiently localizing the sudden concept drifts in process-log.
Capturing the sudden concept drift in process mining
(CEUR-WS, 2015) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
Concept drift is the condition when the process changes during the course of execution. Current methods and analysis techniques existing in process mining are not proficient of analyzing the process which has experienced the concept drift. State-of-the-art process mining approaches consider the process as a static entity and assume that process remains same from beginning of its execution period to end. Emphasis of this paper is to propose the technique for localizing concept drift in control-flow perspective by making use of activity correlation strength feature extracted using process log. Concept drift in the process is localized by applying statistical hypothesis testing methods. The proposed method is verified and validated on few of the real-life and artificial process logs, results obtained are promising in the direction of efficiently localizing the sudden concept drifts in process-log.
Clinical decision support system for early disease detection and management: Statistics-based early disease detection
(IGI Global, 2021) Thomas, L.; M V, M.K.; Annappa, B.
Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error. © 2021, IGI Global.
Concept drifts detection and localisation in process mining
(International Information Institute Ltd. No. 509 Fujimi-Cho 6-64-3 Tachikawa City, Tokyo 190-0013, 2016) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
Process mining provides methods and techniques for analyzing eventlogs recorded in modern information systems that support real-world operations. While analyzing an event-log, techniques in process mining assumes that the process as a static entity. This is not often the case due to possibility of phenomenon called concept drift. During the period of execution, process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with different pace. This paper presents the method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in process-log. © 2016 International Information Institute.
Deep Learning for COVID-19
(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.
Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Discovery of optimal neurons and hidden layers in feed-forward Neural Network
(2016) Thomas, L.; Manoj, Kumar, M.V.; Annappa, B.
Identifying the number of neurons in each hidden layers and number of hidden layers in a multi layered Artificial Neural Network (ANN) is a challenge based on the input data. A new hypothesis is proposed for organizing the synapse from x to y neuron. The synapse of number of neurons to fire between the hidden layer is identified. By the introduction of this hypothesis, an effective number of neurons in multilayered Artificial Neural Network can be identified and self organizing neural network model is developed which is referred as cognitron. The normal brain model has 3 layered perceptron; but the proposed model organizes the number of layers optimal for identifying an effective model. Our result proved that the proposed model constructs a neural model directly by identifying the optimal weights of each neurons and number of neurons in each dynamically identified hidden layers. This optimized model is self organized with different range of neurons on different layer of hidden layer, and by comparing the performance based on computational time and error at each iteration. An efficient number of neurons are organized using gradient decent. The proposed model thus train large model to perform the classification task by inserting optimal layers and neurons. � 2016 IEEE.
Discovery of optimal neurons and hidden layers in feed-forward Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2016) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
Identifying the number of neurons in each hidden layers and number of hidden layers in a multi layered Artificial Neural Network (ANN) is a challenge based on the input data. A new hypothesis is proposed for organizing the synapse from x to y neuron. The synapse of number of neurons to fire between the hidden layer is identified. By the introduction of this hypothesis, an effective number of neurons in multilayered Artificial Neural Network can be identified and self organizing neural network model is developed which is referred as cognitron. The normal brain model has 3 layered perceptron; but the proposed model organizes the number of layers optimal for identifying an effective model. Our result proved that the proposed model constructs a neural model directly by identifying the optimal weights of each neurons and number of neurons in each dynamically identified hidden layers. This optimized model is self organized with different range of neurons on different layer of hidden layer, and by comparing the performance based on computational time and error at each iteration. An efficient number of neurons are organized using gradient decent. The proposed model thus train large model to perform the classification task by inserting optimal layers and neurons. Â© 2016 IEEE.
Distilling lasagna from spaghetti processes
(2017) Manoj, Kumar, M.V.; Thomas, L.; Annappa, B.
If the operational process is flexible, control flow discovery methods in process mining tend to produce Spaghetti (unstructured) models. Spaghetti models generally consist of large number of activities and paths. These models are unstructured, incomprehensible difficult to analyse, impossible to use during operational support and enhancement. Due The structural complexity of Spaghetti processes majority of techniques in process mining can not be applied on them. There is a at most necessity to design and develop methods for simplifying the structure of Spaghetti process to make them easily understandable and reusable. The methods proposed in this paper concentrates on offering the tools and techniques for analysing the Spaghetti process. The problems addressed in this paper are 1) converting the unstructured Spaghetti to structured and simplified Lasagna process, 2) identifying the list of possible, significant, and impossible paths of execution in Lasagna process. The proposed technique is verified and validated on real-life road traffic fine management event-log taken from standard repository. � 2017 ACM.
Distilling lasagna from spaghetti processes
(Association for Computing Machinery acmhelp@acm.org, 2017) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
If the operational process is flexible, control flow discovery methods in process mining tend to produce Spaghetti (unstructured) models. Spaghetti models generally consist of large number of activities and paths. These models are unstructured, incomprehensible difficult to analyse, impossible to use during operational support and enhancement. Due The structural complexity of Spaghetti processes majority of techniques in process mining can not be applied on them. There is a at most necessity to design and develop methods for simplifying the structure of Spaghetti process to make them easily understandable and reusable. The methods proposed in this paper concentrates on offering the tools and techniques for analysing the Spaghetti process. The problems addressed in this paper are 1) converting the unstructured Spaghetti to structured and simplified Lasagna process, 2) identifying the list of possible, significant, and impossible paths of execution in Lasagna process. The proposed technique is verified and validated on real-life road traffic fine management event-log taken from standard repository. Â© 2017 ACM.
Efficient process mining through critical path network analysis
(2014) Thomas, L.; Manoj, Kumar, M.V.; Annappa, B.
Process mining is emerging scientific research discipline, concentrates on discovering, monitoring and enhancing the operational processes using the operational traces of the process documented in log. Process mining enables the process centric analysis of the data and aims at bridging gap between data mining, business process modeling and analysis. This article analyses use of Critical Path Method used in project management, in the context of process mining in order to find critical paths in process model. This article aims in leveraging process mining practices with the application of CPM and study its feasibility. Critical path identifies the minimum time possible to finish the project. Extra care must be taken while executing activities on critical path. Delay in any of the activities on critical path would definitely delay the process completion time and collapse overall process plan. � 2014 IEEE.
Efficient process mining through critical path network analysis
(IEEE Computer Society, 2014) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
Process mining is emerging scientific research discipline, concentrates on discovering, monitoring and enhancing the operational processes using the operational traces of the process documented in log. Process mining enables the process centric analysis of the data and aims at bridging gap between data mining, business process modeling and analysis. This article analyses use of Critical Path Method used in project management, in the context of process mining in order to find critical paths in process model. This article aims in leveraging process mining practices with the application of CPM and study its feasibility. Critical path identifies the minimum time possible to finish the project. Extra care must be taken while executing activities on critical path. Delay in any of the activities on critical path would definitely delay the process completion time and collapse overall process plan. Â© 2014 IEEE.
Foundations of healthcare informatics
(Elsevier, 2021) Annappa, B.; Manoj Kumar, M.V.; Thomas, L.
Health informatics fundamentally deals with the acquisition (recording), processing, interpreting, and using of healthcare (patient) data by domain experts. Healthcare informatics generally refers to the management of data/information in healthcare rather than the application of computers in it-which is centered on patient care. The sheer amount of data and imperfection in decision making imply the usage of information systems (particularly process-aware information systems, called PAIS) in managing the healthcare process. Health informatics mainly offers tools for controlling the healthcare process and facilitating the acquisition of medical knowledge (recording). It offers a reliable and fast communication path among the people involved in the healthcare process. © 2021 Elsevier Inc. All rights reserved.